Many self-monitoring applications today rely on wearable sensors combining inertial (e.g. accelerometer, gyroscope) and physiological (e.g. heart rate, galvanic skin response, skin temperature, respiration) data to develop accurate algorithms. These applications span from physical (e.g. activity type, energy expenditure estimation, fitness estimation) to mental health (e.g. stress, meditation). It has been also noted that better results are reported, for both physical and mental health, when physiological signals (and not only inertial data) are used. However, some main challenges affect state of the art biomedical data analytics. For example, physiological signals differ greatly between individuals and/or change over time due to different factors (people's health, age, etc.). Therefore biomedical data analytics shall take into account not only changes between people, but also changes within an individual over time. When data is not normalized, high error between individuals is reported compared to when data is normalized or individually calibrated.
In the document “Towards Mental Stress Detection Using Wearable Physiological Sensors,” by J. Wijsman et al., IEEE EMBS, pp. 1798-1801, 2011, data is normalized as part of the feature extraction/preparation procedure, before its use as input for the algorithms. This is done applying standard normalization techniques (e.g. remove mean and divide by standard deviation, or divide by range, etc.). However, the procedure is not extendable to new subjects, and for which there is a need to perform the complete protocol again.
In the document “Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure,” by S. Brage, Journal of Applied Physiology, 96(1):343-351, August 2003, data is normalized with respect to a baseline. However, physiological signals do not differ only in offset with respect to a baseline, but also in ranges.
In the document “Hierarchy of individual calibration levels for heart rate and accelerometry to measure physical activity,” by S. Brage et al., J Appl Physiol, 2007, data is normalized during specific protocols, that is, it requires individual calibration. However, calibration needs to be performed using sometimes expensive equipment (e.g. indirect calorimetry for energy expenditure), or requires to be performed very often due to changes in physiological signals over time (e.g. changes in fitness level or age).